Shazizzle

I want to create have written a very basic music matching app similar to Shazam. This repo contains various experiments to help:

• understand some of the concepts
• get some experience using the Web Audio API

Supported Browsers

I have tried it successfully on the following browsers:

• Chrome on macOS
• Firefox on macOS
• Chrome on Android

NOTE: matchTrackWithStreaming.html does not work on Firefox because it doesn't seem to support AudioWorkletNode:

ReferenceError: AudioWorkletNode is not defined [Learn More] pcmObservable.js:1:1
    <anonymous> http://localhost:3002/common/utils/pcmObservable.js:1
    InnerModuleEvaluation self-hosted:4364
    InnerModuleEvaluation self-hosted:4353
    evaluation self-hosted:4317

Database Tracks

You can try it out by playing one of the YouTube links below and clicking Record on one of these pages:

• matchTrack.html
  • capture chosen length of sample then send to server for matching
• matchTrackWithDiagnostics.html
  • as above but with various diagnostic charts
• matchTrackWithStreaming.html
  • stream fingerprinted data to the server over a web socket for progressive matching

I have fingerprinted 12 tracks. I bought them all from iTunes. The .m4a files are not in the repo. However, you can play the tracks on YouTube:

• Walton: Henry V - A Musical Scenario after Shakespeare / Henry V: IV. Interlude: Touch Her Soft Lips and Part
• Cecilia Bartoli - Arie Antiche: Se tu m'ami / Caro mio ben
• Strauss: Vier letzte Lieder, Die Nacht, Allerseelen / Morgen, Op. 27, No. 4
• Stämning (feat. Eric Ericson) [22 Swedish songs] / I Seraillets Have (feat. Eric Ericson)
• Bridge: Piano Music, Vol. 3 / 3 Lyrics: No. 1, Heart's Ease. Andante tranquillo – Lento
• Minimal Piano Collection, Vol. X-XX / Ellis Island for Two Pianos
• Fantasie / Après un rêve
• Victoria: Requiem (Officium defunctorum). Lobo: Versa est in luctum / Taedet animam meam: I. Taedet animam meam
• Too Hot to Handle / Boogie Nights
• Reflections / Gun
• Bach: Cantatas for Alto Solo / Cantata No. 170, BWV 170: I. Aria "Vergnügte Ruh! Beliebte Seelenlust!"
• Haydn: Strings Quartets Op. 71 / Griogal Cridhe "Gregor’s Lament"
  • this is actually a live performance but some of it still matches against the fingerprinted iTunes track!

Buildless Client

Just being lazy really. The client-side code does not need to be built - no Babel, Browserify, webpack, npm run build, etc. This is achieved using the type=module attribute of the <script> tag. Here is an example:

<script type="module" src="matchTrack.js"></script>

Running Locally

After cloning the repo, do the following to run everything locally. This assumes that you have Node.js, npm and Docker installed.

# restore npm packages
npm install

# create a .env file
cat << 'EOF' >> .env
PGSSLMODE=disable
DATABASE_URL=postgres://postgres:mypassword@localhost:5432/postgres
EOF

# run PostgreSQL in a Docker container
scripts/db.sh run

# create database schema
scripts/db.sh create

# restore the database
scripts/db.sh restore

# (optional) show summary of restored database
scripts/db.sh show

# start the Express server
npm start

Then, open a browser to http://localhost:3002.

Or, you can specify a different port number e.g.:

PORT=3200 npm start

TODO

• Copy data from the local Docker database instance to the Heroku database instance
• Store more track metadata e.g. artist and album artwork
• Add more tracks to the database
• Create a new experiment that is a copy of experiment4 but without all the charting
  • i.e. a slimmed-down test page
• Extend experiment4 to include visualisations of the hash matching (more charts!)
  • see Fig 3A and Fig 3B in the original paper
• Add a web page to list the tracks in the database
• Tune the app to perform better. Concentrate on reducing the amount of stored data whilst increasing the ability to find a match. Some of the settings that can be adjusted are:
  • Sample rate (currently 16 kHz)
  • FFT size (currently 1024)
  • Frequency bands (currently 0-100 Hz, 100-200 Hz, 200-400 Hz, 400-800 Hz, 800-1600 Hz, 1600-8000 Hz)
  • Number of slivers (currently 20 per second)
  • Size of the target zone (currently 5 points)
• Write a back end console tool in C# or F# to fingerprint a track and add it to the database
  • Reconcile the FFT & fingerprint data calculated by JavaScript & Web Audio API vs C#/F# & Math.NET Numerics
    • Currently, I am getting results that don't quite match
    • Consequently, I am currently having to use seedTracks.html to fingerprint a track and store it in the database
  • Call out to ffmpeg to convert the track from an mp3/m4a file to pcm
  • Calculate the fingerprint data
  • Store the fingerprint data and track metadata in the database
• Investigate hosting on AWS
  • Load static resources from an Amazon S3 bucket configured for static website hosting
  • Move the match web api endpoint to AWS Lambda
  • Store the track data in Amazon RDS for PostgreSQL
• Implement streaming-based match
  • Rather than capture a 5 second sample and then send it to the server for matching, stream data to the server and return a match as soon as possible e.g. after 2 seconds.

Test Signals

• Audio Test Tones (Spotify)

Links

• Web Audio API
• MediaStream Recording API
• Enter Audio Worklet
• An Industrial-Strength Audio Search Algorithm
• How does Shazam work
• Fourier - Math.NET Numerics Documentation